Despite considerable efforts in developing curve-fitting protocols to evaluate the crystallinity index (CI) from X-ray diffraction (XRD) measurements, in its present state XRD can only provide a qualitative or semi-quantitative assessment of the amounts of crystalline or amorphous fraction in a sample. The greatest barrier to establishing quantitative XRD is the lack of appropriate cellulose standards, which are needed to calibrate the XRD measurements. In practice, samples with known CI are very difficult to prepare or determine. In a previous study, ¹⁴ we reported the development of a simple algorithm for determining fiber crystallinity information from Fourier transform infrared (FT-IR) spectroscopy. Hence, in this study we not only compared the fiber crystallinity information between FT-IR and XRD measurements, by developing a simple XRD algorithm in place of a time-consuming and subjective curve-fitting process, but we also suggested a direct way of determining cotton cellulose CI by calibrating XRD with the use of CI_IR as references.

With cotton fiber growth or maturation, cellulose content in cotton fibers markedly increases. Traditional chemical methods have been developed to determine cellulose content, but it is time-consuming and labor-intensive, mostly owing to the slow hydrolysis process of fiber cellulose components. As one approach, the attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy technique has also been utilized to monitor cotton cellulose formation, by implementing various spectral interpretation strategies of both multivariate principal component analysis (PCA) and 1-, 2- or 3-band/-variable intensity or intensity ratios. The main objective of this study was to compare the correlations between cellulose content determined by chemical analysis and ATR FT-IR spectral indices acquired by the reported procedures, among developmental Texas Marker-1 (TM-1) and immature fiber (im) mutant cotton fibers. It was observed that the R value, CI_IR, and the integrated intensity of the 895 cm⁻¹ band exhibited strong and linear relationships with cellulose content. The results have demonstrated the suitability and utility of ATR FT-IR spectroscopy, combined with a simple algorithm analysis, in assessing cotton fiber cellulose content, maturity, and crystallinity in a manner which is rapid, routine, and non-destructive.

Cotton fibers are natural plant products, and their end-use qualities depend on their stages of development. In general, the quantity of cellulose in cotton fibers increases rapidly, thus it leads to compositional, structural and physical attribute variations among the fibers with shorter and longer growth periods. This article discusses recent progress in applying the Fourier transform infrared (FTIR) spectroscopic technique to characterize these differences, to discriminate immature fibers from mature fibers, to assess fiber maturity and crystallinity and also to unravel the band assignments in crystalline and amorphous celluloses. The results were achieved through the use of various strategies, including wet chemical analysis, principal component analysis (PCA), simple algorithm development, two-dimensional correlation analysis and other independent fiber tests. Of particular interest is that, in general, immature fibers might have the characteristics of less than 21–28 dpa, M_IR < 0.58 (in the maturity range of 0 to 1.0) and CI_IR < 42% (in the crystallinity range of 0 to 100%).

Individual fiber strength is an important quality attribute that greatly influences the strength of the yarn spun from cotton fibers. Fiber strength is usually measured from bundles of fibers due to the difficulty of reliably measuring strength from individual cotton fibers. However, bundle fiber strength (BFS) is not always correlated with yarn strength since it is affected by multiple fiber properties involved in fiber-to-fiber interactions within a bundle in addition to the individual fiber strength. Molecular mechanisms responsible for regulating individual fiber strength remain unknown. Gossypium hirsutum near isogenic lines (NILs), MD52ne and MD90ne showing variations in BFS provide an opportunity for dissecting the regulatory mechanisms involved in individual fiber strength. Comprehensive fiber property analyses of the NILs revealed that the superior bundle strength of MD52ne fibers resulted from high individual fiber strength with minor contributions from greater fiber length. Comparative transcriptome analyses of the NILs showed that the superior bundle strength of MD52ne fibers was potentially related to two signaling pathways: one is ethylene and the interconnected phytohormonal pathways that are involved in cotton fiber elongation, and the other is receptor-like kinases (RLKs) signaling pathways that are involved in maintaining cell wall integrity. Multiple RLKs were differentially expressed in MD52ne fibers and localized in genomic regions encompassing the strength quantitative trait loci (QTLs). Several candidate genes involved in crystalline cellulose assembly were also up-regulated in MD52ne fibers while the secondary cell wall was produced. Comparative phenotypic and transcriptomic analyses revealed differential expressions of the genes involved in crystalline cellulose assembly, ethylene and RLK signaling pathways between the MD52ne and MD90ne developing fibers. Ethylene and its phytohormonal network might promote the elongation of MD52ne fibers and indirectly contribute to the bundle strength by potentially improving fiber-to-fiber interactions. RLKs that were suggested to mediate a coordination of cell elongation and SCW biosynthesis in other plants might be candidate genes for regulating cotton fiber cell wall assembly and strength.

Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples, and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis-NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B ₁ (AFB ₁ ). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in 50:50 (v/v) methanol/water onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB ₁ -contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis-NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB ₁ contamination of peanut kernels.